Variability of in vivo potency assays of whole-cell pertussis, inactivated polio, and meningococcal B vaccines.

For the batch release of vaccines, potency release assays are required. Non-animal in vitro tests have numerous advantages and are preferred; however, several vaccines are still released using in vivo assays. Their major drawback is the inherent variability with its practical implications. We quantified the variability of in vivo potency release assays for whole-cell pertussis, inactivated polio and meningococcal B (MenB) vaccines which showed large CV (Coefficient of Variation) ranging from 34% to 125%. As inherent variability might potentially be attributed to the highly variable immune system between individual animals, we evaluated the antibody titres to four MenB antigens in 344 individual outbred mice. These varied strongly, with more than 100-fold differences in antibody titres in responsive mice. Furthermore, within individual mice there was generally no correlation between the strengths of the responses to the four antigens. A mouse with a very low or no response to one antigen in many cases exhibited a strong response to another antigen. The large differences between individual animals is likely a considerable contributor to the inherent variability of in vivo potency assays. Our data again support the notion that it is preferred to move away from in vivo potency assays for monitoring batch to batch consistency as part of vaccine batch release testing.

Selecting relevant moderators with Bayesian regularized meta-regression.

When meta-analyzing heterogeneous bodies of literature, meta-regression can be used to account for potentially relevant between-studies differences. A key challenge is that the number of candidate moderators is often high relative to the number of studies. This introduces risks of overfitting, spurious results, and model non-convergence. To overcome these challenges, we introduce Bayesian Regularized Meta-Analysis (BRMA), which selects relevant moderators from a larger set of candidates by shrinking small regression coefficients towards zero with regularizing (LASSO or horseshoe) priors. This method is suitable when there are many potential moderators, but it is not known beforehand which of them are relevant. A simulation study compared BRMA against state-of-the-art random effects meta-regression using restricted maximum likelihood (RMA). Results indicated that BRMA outperformed RMA on three metrics: BRMA had superior predictive performance, which means that the results generalized better; BRMA was better at rejecting irrelevant moderators, and worse at detecting true effects of relevant moderators, while the overall proportion of Type I and Type II errors was equivalent to RMA. BRMA regression coefficients were slightly biased towards zero (by design), but its residual heterogeneity estimates were less biased than those of RMA. BRMA performed well with as few as 20 studies, suggesting its suitability as a small sample solution. We present free open source software implementations in the R-package pema (for penalized meta-analysis) and in the stand-alone statistical program JASP. An applied example demonstrates the use of the R-package.